Infrared point target detection with improved template matching

Liu, Ruiming; Lu, Yifeng; Gong, Chenglong; Liu, Yang

doi:10.1016/j.infrared.2012.01.006

Cited by 80 publications

(29 citation statements)

References 18 publications

(30 reference statements)

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“…In this paper, we have presented three concepts that have been extensively used in image matching fields: templates [5,31], landmarks [31,33] and trust regions [32].…”

Section: Related Workmentioning

confidence: 99%

Predefined pattern detection in large time series

Miao

Vespier

Cachucho

et al. 2016

Information Sciences

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a b s t r a c tPredefined pattern detection from time series is an interesting and challenging task. In order to reduce its computational cost and increase effectiveness, a number of time series representation methods and similarity measures have been proposed. Most of the existing methods focus on full sequence matching, that is, sequences with clearly defined beginnings and endings, where all data points contribute to the match. These methods, however, do not account for temporal and magnitude deformations in the data and result to be ineffective on several real-world scenarios where noise and external phenomena introduce diversity in the class of patterns to be matched. In this paper, we present a novel pattern detection method, which is based on the notions of templates, landmarks, constraints and trust regions. We employ the Minimum Description Length (MDL) principle for time series preprocessing step, which helps to preserve all the prominent features and prevents the template from overfitting. Templates are provided by common users or domain experts, and represent interesting patterns we want to detect from time series. Instead of utilising templates to match all the potential subsequences in the time series, we translate the time series and templates into landmark sequences, and detect patterns from landmark sequence of the time series. Through defining constraints within the template landmark sequence, we effectively extract all the landmark subsequences from the time series landmark sequence, and obtain a number of landmark segments (time series subsequences or instances). We model each landmark segment through scaling the template in both temporal and magnitude dimensions. To suppress the influence of noise, we introduce the concept of trust region, which not only helps to achieve an improved instance model, but also helps to catch the accurate boundaries of instances of the given template. Based on the similarities derived from instance models, we introduce the probability density function to calculate a similarity threshold. The threshold can be used to judge if a landmark segment is a true instance of the given template or not. To evaluate the effectiveness and efficiency of the proposed method, we apply it to two real-world datasets. The results show that our method is capable of detecting patterns of temporal and magnitude deformations with competitive performance.

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“…In this paper, we have presented three concepts that have been extensively used in image matching fields: templates [5,31], landmarks [31,33] and trust regions [32].…”

Section: Related Workmentioning

confidence: 99%

Predefined pattern detection in large time series

Miao

Vespier

Cachucho

et al. 2016

Information Sciences

View full text Add to dashboard Cite

show abstract

“…Template matching technique has been extensively studied and used in pattern recognition application, and some template matching methods for infrared small detection based on normalized crosscorrelation was presented [21,22]. In addition, principal component analysis (PCA) can reduce redundancies in high dimensional by using low dimensional linear subspace, the projection coefficients templates obtained from principal component analysis (PCA) and the nonlinear correlation similarity measurement were proposed for template matching [23,24]. Meanwhile, according to the principle of point spread function (PSF), small target training sample images were generated and extracted by principal component analysis (PCA) [25], probabilistic principal component analysis (PPCA) [26], nonlinear principal component analysis (NLPCA) [27] and kernel principal component analysis (KPCA) [28] to establish the principal component subspace, and the similarity between original image block and reconstructed one could indicate it is a true target or not.…”

Section: Related Workmentioning

confidence: 99%

Adaptive detection method of infrared small target based on target-background separation via robust principal component analysis

Wang

Qin

2015

Infrared Physics & Technology

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“…As a key technology of infrared imaging system, infrared small target detection has been paid more and more attention in recent years [1][2][3][4][5]. On the one hand, Due to long imaging distance, targets occupy only a few pixels in the infrared images.…”

Section: Introductionmentioning

confidence: 99%